Abstract:
The present disclosure includes methods and systems for correcting distortions in spherical panorama digital images. In particular, one or more embodiments of the disclosed systems and methods correct for tilt and/or roll in a digital camera utilized to capture a spherical panorama digital images by determining a corrected orientation and generating an enhanced spherical panorama digital image based on the corrected orientation. In particular, in one or more embodiments, the disclosed systems and methods identify line segments in a spherical panorama digital image, map the line segments to a three-dimensional space, generate great circles based on the identified line segments, and determine a corrected orientation based on the generated great circles.
Abstract:
An image processing application performs improved face exposure correction on an input image. The image processing application receives an input image having a face and ascertains a median luminance associated with a face region corresponding to the face. The image processing application determines whether the median luminance is less than a threshold luminance. If the median luminance is less than the threshold luminance, the application computes weights based on a spatial distance parameter and a similarity parameter associated with the median chrominance of the face region. The image processing application then computes a corrected luminance using the weights and applies the corrected luminance to the input image. The image processing application can also perform improved face color correction by utilizing stylization-induced shifts in skin tone color to control how aggressively stylization is applied to an image.
Abstract:
Techniques and systems are described to generate a compact video feature representation for sequences of frames in a video. In one example, values of features are extracted from each frame of a plurality of frames of a video using machine learning, e.g., through use of a convolutional neural network. A video feature representation is generated of temporal order dynamics of the video, e.g., through use of a recurrent neural network. For example, a maximum value is maintained of each feature of the plurality of features that has been reached for the plurality of frames in the video. A timestamp is also maintained as indicative of when the maximum value is reached for each feature of the plurality of features. The video feature representation is then output as a basis to determine similarity of the video with at least one other video based on the video feature representation.
Abstract:
Systems and methods are provided for content-based selection of style examples used in image stylization operations. For example, training images can be used to identify example stylized images that will generate high-quality stylized images when stylizing input images having certain types of semantic content. In one example, a processing device determines which example stylized images are more suitable for use with certain types of semantic content represented by training images. In response to receiving or otherwise accessing an input image, the processing device analyzes the semantic content of the input image, matches the input image to at least one training image with similar semantic content, and selects at least one example stylized image that has been previously matched to one or more training images having that type of semantic content. The processing device modifies color or contrast information for the input image using the selected example stylized image.